The present disclosure relates to prognostics for a device, and in particular, a system and method for estimating remaining life for the device.
Estimating a remaining life of equipment is known as prognostics. Remaining life estimates provide valuable information for operation of equipment. Remaining life estimates provide decision making aids that allow operators to change operational characteristics (such as load), which in turn may prolong the life of the equipment. Remaining life estimates also allow planners to account for upcoming maintenance and set in motion a logistics process that supports a smooth transition from faulted to fully functioning equipment. Predicting remaining life is not straightforward because, ordinarily, remaining life is dependent upon future usage parameters, such as load and speed. In addition, an understanding of the underlying physics that govern remaining life is hard to ascertain for complex equipment where numerous fault modes can potentially be the driver for remaining life.
A common approach to prognostics is to employ a model of damage propagation contingent on future use. Such a model is often based on detailed materials knowledge and makes use of finite element modeling. Because such models are extremely costly to develop, they are limited to a few important parts of a subsystem, but are rarely applied to a full system.
Another known approach for estimating remaining life is a data-driven approach where equipment behavior is tracked via sensor measurements during normal operation throughout the useful life of the equipment. The end of equipment useful life can represent a totally non-functioning state of the equipment for example, equipment failure. The end of equipment useful life can also represent a state of the equipment wherein the equipment no longer provides expected results. Pattern recognition algorithms can be employed to recognize trends and predict remaining life. This approach provides voluminous amounts of data resulting in expensive algorithms to process the data. Further, these predictions are often made under an assumption of near-constant future load parameters.
Known power equipment rarely operates under near-constant load parameters. Wind turbines, for example, demand cost-effective solutions capable of operating under severe & variable conditions. Product end-of-life and unscheduled downtime may vary significantly from turbine to turbine, complicating design predictions and fleet maintenance. The exposure to cyclic stress varies dramatically from turbine to turbine, and provision of margin for the most demanding turbine results in excessive cost to the majority. Further expectations of known cyclic fatigue typically are based on manufacturer's predefined cycles to failure, wherein known turbine operation rarely repeats those manufacturers' predefined cycles.